Title of Paper:Stability Analysis for Neural Networks With Time-Varying Delay Based on Quadratic Convex Combination

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Date of Publication:2013-04-01

Journal:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Included Journals:SCIE、EI、ESI高被引论文、Scopus

Volume:24

Issue:4

Page Number:513-521

ISSN No.:2162-237X

Key Words:Quadratic convex combination; recurrent neural network (RNN); stability analysis; time-varying delay

Abstract:In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.